Future networks (including 6G) are poised to accelerate the realisation of
Internet of Everything. Cependant, it will result in a high demand for computing
resources to support new services. Mobile Edge Computing (MEC) is a promising
solution, enabling to offload computation-intensive tasks to nearby edge
servers from the end-user devices, thereby reducing latency and energy
consumption. Cependant, relying solely on a single MEC server for task offloading
can lead to uneven resource utilisation and suboptimal performance in complex
scenarios. Additionally, traditional task offloading strategies specialise in
centralised policy decisions, which unavoidably entail extreme transmission
latency and reach computational bottleneck. To fill the gaps, we propose a
latency and energy efficient Cooperative Task Offloading framework with
Transformer-driven Prediction (CTO-TP), leveraging asynchronous multi-agent
deep reinforcement learning to address these challenges. This approach fosters
edge-edge cooperation and decreases the synchronous waiting time by performing
asynchronous training, optimising task offloading, and resource allocation
across distributed networks. The performance evaluation demonstrates that the
proposed CTO-TP algorithm reduces up to 80% overall system latency and 87%
energy consumption compared to the baseline schemes.

Cet article explore les excursions dans le temps et leurs implications.

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